Published on in Vol 20 , No 4 (2018) :April

Preprints (earlier versions) of this paper are available at, first published .
Risk Assessment for Parents Who Suspect Their Child Has Autism Spectrum Disorder: Machine Learning Approach

Risk Assessment for Parents Who Suspect Their Child Has Autism Spectrum Disorder: Machine Learning Approach

Risk Assessment for Parents Who Suspect Their Child Has Autism Spectrum Disorder: Machine Learning Approach


  1. Bokobza C, Van Steenwinckel J, Mani S, Mezger V, Fleiss B, Gressens P. Neuroinflammation in preterm babies and autism spectrum disorders. Pediatric Research 2019;85(2):155 View
  2. Barbaro J, Yaari M. Study protocol for an evaluation of ASDetect - a Mobile application for the early detection of autism. BMC Pediatrics 2020;20(1) View
  3. McCarty P, Frye R. Early Detection and Diagnosis of Autism Spectrum Disorder: Why Is It So Difficult?. Seminars in Pediatric Neurology 2020;35:100831 View
  4. Sadilek A, Hswen Y, Bavadekar S, Shekel T, Brownstein J, Gabrilovich E. Lymelight: forecasting Lyme disease risk using web search data. npj Digital Medicine 2020;3(1) View
  5. Washington P, Kalantarian H, Tariq Q, Schwartz J, Dunlap K, Chrisman B, Varma M, Ning M, Kline A, Stockham N, Paskov K, Voss C, Haber N, Wall D. Validity of Online Screening for Autism: Crowdsourcing Study Comparing Paid and Unpaid Diagnostic Tasks. Journal of Medical Internet Research 2019;21(5):e13668 View
  6. Dahiya A, DeLucia E, McDonnell C, Scarpa A. A systematic review of technological approaches for autism spectrum disorder assessment in children: Implications for the COVID-19 pandemic. Research in Developmental Disabilities 2021;109:103852 View
  7. Desideri L, Pérez-Fuster P, Herrera G. Information and Communication Technologies to Support Early Screening of Autism Spectrum Disorder: A Systematic Review. Children 2021;8(2):93 View
  8. Wang H, Avillach P. Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants in Autism Spectrum Disorder: Genotype-Based Deep Learning. JMIR Medical Informatics 2021;9(4):e24754 View
  9. Johora F. The preschool teacher’s assumptions about a child’s ability or disability: finding a pedagogical password for inclusion. Early Years 2023;43(1):197 View
  10. Albahri A, Zaidan A, AlSattar H, Hamid R, Albahri O, Qahtan S, Alamoodi A. Towards physician's experience: Development of machine learning model for the diagnosis of autism spectrum disorders based on complex  T ‐spherical fuzzy‐weighted zero‐inconsistency method. Computational Intelligence 2023;39(2):225 View
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  12. Boch S, Hussain S, Bambach S, DeShetler C, Chisolm D, Linwood S. Locating Youth Exposed to Parental Justice Involvement in the Electronic Health Record: Development of a Natural Language Processing Model. JMIR Pediatrics and Parenting 2022;5(1):e33614 View
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Books/Policy Documents

  1. Usta M. Neural Engineering Techniques for Autism Spectrum Disorder. View
  2. Dahiya A, Bertollo J, McDonnell C, Scarpa A. Neural Engineering Techniques for Autism Spectrum Disorder, Volume 2. View